Markov chain Monte Carlo methods for state-space models with point process observations
Yuan, Ke, Girolami, Mark and Niranjan, Mahesan (2012) Markov chain Monte Carlo methods for state-space models with point process observations. Neural Computation, 24, (6), 1462-1486. (doi:10.1162/NECO_a_00281). (PMID:22364499).
This letter considers how a number of modern Markov chain Monte Carlo (MCMC) methods can be applied for parameter estimation and inference in state-space models with point process observations. We quantified the efficiencies of these MCMC methods on synthetic data, and our results suggest that the Reimannian manifold Hamiltonian Monte Carlo method offers the best performance. We further compared such a method with a previously tested variational Bayes method on two experimental data sets. Results indicate similar performance on the large data sets and superior performance on small ones. The work offers an extensive suite of MCMC algorithms evaluated on an important class of models for physiological signal analysis.
|Digital Object Identifier (DOI):||doi:10.1162/NECO_a_00281|
|Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QP Physiology
|Divisions:||Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Southampton Wireless Group
|Date Deposited:||01 May 2012 08:02|
|Last Modified:||31 Mar 2016 14:27|
|Further Information:||Google Scholar|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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